Associative memories, also referred to as content addressable memories, are widely used in the field of pattern matching and identification, expert systems and artificial intelligence. A widely used associative memory is the Hopfield artificial neural network. Hopfield artificial neural networks are described, for example, in U.S. Pat. No. 4,660,166 to Hopfield entitled “Electronic Network for Collective Decision Based on Large Number of Connections Between Signals”.
Although associative memories may avoid problems in prior back-propagation networks, associative memories may present problems of scaling and spurious memories. Recent improvements in associative memories have attempted to solve these and other problems. For example, U.S. Pat. No. 6,052,679 to coinventor Aparicio, I V et al., entitled “Artificial Neural Networks Including Boolean-Complete Compartments” provides a plurality of artificial neurons and a plurality of Boolean-complete compartments, a respective one of which couples a respective pair of artificial neurons.
Beyond single-point neuron models of traditional neural networks, real neurons exhibit complex, nonlinear behavior equivalent to networks within themselves. In particular, recent computational neuroscience has focused on understanding the neuron's wiring efficiency and computational power, particularly in how dendrites (structurally linear) compute coincidences (functionally non-linear). However, a computational level of analysis to better understand neuronal dendrites as well as to develop neuromorphic devices has remained elusive. The answer is found by assuming a coincidence matrix (a graph) as the fundamental object of each neuron's memory but without requiring an explicit crossbar as typical of many current neuromorphic efforts. Tomographic projections of each matrix are shown represent a lossless compression, expressible by cascading waves of synaptic activation over a receptivity array of dendritic compartments. This simple activation-passing algorithm is capable of reading and writing graph structures. Such wiring efficiency explains how each neuron represents a nonlinear associative memory and inspires emergent neuromorphic devices to store and compute such memories without the cost of geometric crossbars. Matrix bandwidth reduction adds even greater processing speed, and logical reversibility promises adiabatic energy efficiency. As Cognitive Computing continues to emerge as the basis for machine intelligence, a more brain-like approach will move into operating systems, and will ultimately require wiring and energy efficiencies to support cognition by cognitive hardware.